Nice to have: Experience building AI products for healthcare organizations; experience training or pre-training LLMs, including data curation, distributed training, and domain-specific model evaluation; knowledge of the U.S. healthcare system, including payer and provider operations, reimbursement models, and healthcare regulations; publications, patents, or open-source contributions in AI/ML or clinical NLP. Strong expertise in end-to-end AI solution design, classical ML, LLMs and document AI, agentic AI, RAG pipelines, vector databases, Python, SQL, CI/CD, Docker, Kubernetes, AWS, MLflow, observability tools, and data pipelines using Spark or Airflow.